Methods and systems for natural language processing of graph database queries

This invention helps people ask questions about data stored in a graph database using everyday language, instead of needing to learn complex database commands. It works by using artificial intelligence to first understand what key things (like names or places) are mentioned in your question, and then figure out what you're trying to achieve (your "intent"). It then uses this understanding to build the correct technical query for the graph database, often using pre-defined templates. The claims specifically focus on using two machine learning models for this, trained on data from particular fields like cybersecurity or military operations.

Why it matters: Filed in 2024, the core challenge was building and integrating multiple specialized machine learning models for natural language processing and query generation. By 2026, the capabilities of large language models (LLMs) for complex reasoning, structured output, and efficient fine-tuning on domain-specific data have matured, potentially simplifying the architecture and development effort required to implement such a system.

Status: Under examinationA pending application. It has not been granted, and it has not been abandoned.
How hard to build: SpecializedHeavy software system, ML models, domain-specific data

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